... Arthropod Satellites Have Very Short Identical Repeats 119 fiiifelt Mammalian Satellites Consist of Hierarchical ... ... The Transcription Reaction Has Three Stages 260 ifll Phage T7 RNA Polymerase Is a Useful Model System ... ... A Model for Enzyme Movement Is Suggested by the Crystal Structure 262 -- ??? ... ... DNA Synthesis Is Semidiscontinuous 434 tfefci The (pX Model System Shows How Single-Stranded DNA Is Generated ...

... Data with Errors in Both Coordinates 785 -- 15.4 General Linear Least Squares 788 -- 15.5 Nonlinear Models ... ... 799 -- 15.6 Confidence Limits on Estimated Model Parameters 807 -- 15.7 Robust Estimation 818 -- 15.8 ... ... Regression 836 -- 16 Classification and Inference 840 -- 16.0 Introduction 840 -- 16.1 Gaussian Mixture Models ... ... and k-Means Clustering 842 -- 16.2 Viterbi Decoding 850 -- 16.3 Markov Models and Hidden Markov Modeling ... ... 856 -- 16.4 Hierarchical Clustering by Phylogenetic Trees 868 -- 16.5 Support Vector Machines 883 -- ...

... 9.10 Problems 337 -- 10 Clustering and Trees 339 -- 10.1 Gene Expression Analysis 339 -- 10.2 Hierarchical ... ... Reconstruction 358 -- 10.7 Reconstructing Trees from Additive Matrices 361 -- 10.8 Evolutionary Trees and Hierarchical ... ... 10.12 Notes 379 -- Biobox: Ron Shamir 380 -- 10.13 Problems 384 -- Contents xiii -- 11 Hidden Markov Models ... ... -- 11.1 CG-Islands and the \"Fair Bet Casino\" 387 -- 11.2 The Fair Bet Casino and Hidden Markov Models ...

... Effects of Different Guideline Conceptualizations on the Example of the HL7 Reference Information Model ... ... : -- Schadow G 88 -- Modelling Clinical Goals: A Corpus of Examples and a Tentative Ontology: -- FoxJ ... ... Ontologies and Medical Information Systems: -- Correndo G., Terenziani P 91 -- The SAGE Guideline Modeling ... ... -- Chapter IV: Symposium on Biomedical Informatics and Biomedical -- Statistics Education 99 -- Modeling ... ... the Components of EPR Related Education: -- Hasman A 103 -- Comparative Analysis of Typologies of Modeling ...

... Second Edition -- IX -- Preface to the First Edition XIII -- 1 Introduction 1 -- 1.1 Statistical Models ... ... Annealing 163 -- 5.2.4 Prior Feedback 169 -- 5.3 Stochastic Approximation 174 -- 5.3.1 Missing Data Models ... ... 422 -- Variable Dimension Models and Reversible Jump Algorithms 425 -- 11.1 Variable Dimension Models ... ... 425 -- 11.1.1 Bayesian Model Choice 426 -- 11.1.2 Difficulties in Model Choice 427 -- 11.2 Reversible ... ... History of Particle Systems 577 -- 14.6.2 Dynamic Importance Sampling 577 -- 1. 14.6.3 Hidden Markov Models ...

... Selection -- 3.4.1 Choosing a Set of Candidate Models -- 3.4.2 Choosing the \"Best\" Model -- 3.4.3 ... ... -- 4.5.2 The Negative Binomial Model -- 4.5.3 Overdispersed Binomial Data and the -- Beta-Binomial Model ... ... Model 125 -- 5.1.2 Estimation in the Generalized Linear Model 127 -- 5.1.3 Hypothesis Tests and Confidence ... ... for Three-Dimensional Tables 318 -- 8.2.1 Hierarchical Loglinear Models 318 -- 8.2.2 Association Diagrams ... ... 365 -- 9.1.2 Inference for the Logistic Regression Model 369 -- 9.1.3 Model Fit and Model Selection 372 ...

... 1 The Basics 1 -- 1.1 Distinguish Randomized and Observational Studies 2 -- 1.2 Beware of Linear Models ... ... - 1.3 Understand Omnibus Quantities 6 -- 1.4 Independence, Equal Variance, and Normality 7 -- 1.5 Models ... ... Ten Subjects for Every Variable -- Investigated 87 -- 4.7 Begin with the Exponential Distribution to Model ... ... 4.11 Do Not Dichotomize Unless Absolutely Necessary 99 -- 4.12 Select an Additive or Multiplicative Model ... ... 109 -- 5.6 Consider the Size of the Population Affected by Small -- Effects 111 -- 5.7 Statistical Models ...